#注释1: 导入tensorflow, random, os等包
import tensorflow as tf
import random
import os
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = 2
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #改错 增加引号

#注释2：从minist包中导入输入数据input_data
from tensorflow.examples.tutorials.mnist import input_data
#注释3；设置随机种子
tf.set_random_seed(777)

# mnist = input_data.read_data_sets("D:\Deep learning_code\MNIST_data", one_hot=True)
#读取mnist数据集
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #改错，数据文件所在的目录

#注释4：设置学习率参数
learning_rate = 0.001
#注释5：设置迭代次数
training_epochs = 15
#注释6：设置批次大小
batch_size = 100

# TB_SUMMARY_DIR = ' '
TB_SUMMARY_DIR = 'logs/t1'   # 定义tensorboard日志存储目录

#注释7：设置样本集数据的X,Y的占位符
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

#注释8：设置输入图片的维度(?, 28,28,1)
x_image = tf.reshape(X, [-1, 28, 28, 1])
#注释9: 记录输入图片summary
tf.summary.image('input', x_image, 3)  # max_images=3,选取最多不超过3张图片


#注释10：设置dropou
# t参数
keep_prob = tf.placeholder(tf.float32)   #keep_prob：神经网络每一层保留的单元比例，防止过拟合，需要去掉一些单元

#注释11: 设置第1层网络参数
with tf.variable_scope('layer1') as scope: # 定义计算图的第一层layer1
    W1 = tf.get_variable("W", shape=[784, 512],       #第一个隐藏层：512个单元
                         initializer=tf.contrib.layers.xavier_initializer())  #采用xavier初始化方法
    b1 = tf.Variable(tf.random_normal([512]) )  #改错增加右括号
    ##注释12：计算第1层神经网络的激活函数relu的输出
    L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
    ##注释13：计算dropout后的神经网络输出
    L1 = tf.nn.dropout(L1, keep_prob=keep_prob)  #dropout去掉网络单元，keep_prob=保留的比例
    # 注释14：记录X, W1, b1, L1的summary
    tf.summary.histogram("X", X)  #记录X的历史记录直方图histogram
    tf.summary.histogram("weights", W1) #记录w的历史记录直方图histogram
    tf.summary.histogram("bias", b1)
    tf.summary.histogram("layer", L1)

with tf.variable_scope('layer2') as scope: #定义计算图的第一层layer1
    W2 = tf.get_variable("W", shape=[512, 512], #第二层隐藏层：单元个数512
                         initializer=tf.contrib.layers.xavier_initializer())
    b2 = tf.Variable(tf.random_normal([512]))
    L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
    L2 = tf.nn.dropout(L2, keep_prob=keep_prob)

    #记录W2, b2, L2的summary
    tf.summary.histogram("weights", W2)
    tf.summary.histogram("bias", b2)
    tf.summary.histogram("layer", L2)

with tf.variable_scope('layer3') as scope:
    W3 = tf.get_variable("W", shape=[512, 512],#第三层隐藏层：单元个数512
                         initializer=tf.contrib.layers.xavier_initializer())
    b3 = tf.Variable(tf.random_normal([512]))
    # L3 = tf.relu(tf.matmul(L2, W3) + b3)
    L3 = tf.nn.relu(tf.matmul(L2, W3) + b3)#改错增加nn
    # L3 = tf.dropout(L3, keep_prob=keep_prob)
    L3 = tf.nn.dropout(L3, keep_prob=keep_prob) #改错增加 nn

    tf.summary.histogram("weights", W3)
    tf.summary.histogram("bias", b3)
    tf.summary.histogram("layer", L3)

with tf.variable_scope('layer4') as scope:
    W4 = tf.get_variable("W", shape=[512, 512],
                         initializer=tf.contrib.layers.xavier_initializer())
    b4 = tf.Variable(tf.random_normal([512]))
    L4 = tf.nn.relu(tf.matmul(L3, W4) + b4)
    L4 = tf.nn.dropout(L4, keep_prob=keep_prob)

    tf.summary.histogram("weights", W4)
    tf.summary.histogram("bias", b4)
    tf.summary.histogram("layer", L4)

with tf.variable_scope('layer5') as scope:
    W5 = tf.get_variable("W", shape=[512, 10],
                         initializer=tf.contrib.layers.xavier_initializer())
    b5 = tf.Variable(tf.random_normal([10]))
    hypothesis = tf.matmul(L4, W5) + b5

    tf.summary.histogram("weights", W5)
    tf.summary.histogram("bias", b5)
    tf.summary.histogram("hypothesis", hypothesis)


#注释15: 计算softmax交叉熵代价
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=hypothesis, labels=Y))
#注释16：设置Adam优化器，求解最小代价
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
#注释17：记录代价summary
tf.summary.scalar("loss", cost)  #记录标量scalar---代价

#注释18：汇总所有的摘要图summary
summary = tf.summary.merge_all()

#注释19: 创建会话
# sess = tf.Session()
sess = tf.Session() #改错 去掉 #
sess.run(tf.global_variables_initializer())

#注释20：设置summary的文件对象FileWriter，并指定目录
# writer = summary.FileWriter(TB_SUMMARY_DIR)
writer = tf.summary.FileWriter(TB_SUMMARY_DIR) #改错 增加 tf
writer.add_graph(sess.graph)   #写入计算图
global_step = 0  #总步数

print('Start learning!')

#注释21: 训练模型
for epoch in range(training_epochs):  #迭代轮次， epoch:代/轮
    avg_cost = 0  #每一代的平均代价
    total_batch = int(mnist.train.num_examples / batch_size)  #计算总批次数, total_batch =550

    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: 0.7}  # 注意keep_prob: 0.7, 保留70%神经元
        s, _, = sess.run([summary, optimizer], feed_dict=feed_dict)
        writer.add_summary(s, global_step=global_step)  #写入summary
        global_step += 1  #更新总步数

        #记录每一代平均代价
        avg_cost += sess.run(cost, feed_dict=feed_dict) / total_batch

    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))

print('Learning Finished!')

#注释22：比较预测值和真实值
correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))
#注释23：计算测试集准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print('Accuracy:', sess.run(accuracy, feed_dict={
      X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1}))

##注释24：随机从测试集选取一张图片，输出预测值和真实值
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", sess.run(
    # tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]; keep_prob: 1}))
tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1], keep_prob: 1})) #改错分号改为逗号

